Table of Contents

A Non-Technical Introduction to Machine Learning.- Classic Machine Learning Methods.- Deep Learning: Basics and Convolutional Neural Networks (CNN).- Recurrent Neural Networks (RNN) - Architectures, Training Tricks, and Introduction to Influential Research.- Generative Adversarial Networks and Other Generative Models.- Transformers and Visual Transformers.- Clinical Assessment of Brain Disorders.- Neuroimaging in Machine Learning for Brain Disorders.- Electroencephalography and Magnetoencephalography.- Working with Omics Data, An Interdisciplinary Challenge at the Crossroads of Biology and Computer Science.- Electronic Health Records as Source of Research Data.- Mobile Devices, Connected Objects, and Sensors.- Medical Image Segmentation using Deep Learning.- Image Registration: Fundamentals and Recent Advances Based on Deep Learning.- Computer-Aided Diagnosis and Prediction in Brain Disorders.- Subtyping Brain Diseases from Imaging Data.- Data-Driven Disease Progression Modelling.- Computational Pathology for Brain Disorders.- Integration of Multimodal Data.- Evaluating Machine Learning Models and their Diagnostic Value.- Reproducibility in Machine Learning for Medical Imaging.- Interpretability of Machine Learning Methods Applied to Neuroimaging.- A Regulatory Science Perspective on Performance Assessment of Machine Learning Algorithms in Imaging.- Main Existing Datasets for Open Brain Research on Humans.- Machine Learning for Alzheimer’s Disease and Related Dementias.- Machine Learning for Parkinson’s Disease and Related Disorders.- Machine Learning in Neuroimaging of Epilepsy.- Machine Learning in Multiple Sclerosis.- Machine Learning for Cerebrovascular Disorders.- The Role of Artificial Intelligence in Neuro-Oncology Imaging.- Machine Learning for Neurodevelopmental Disorders.- Machine Learning and BrainImaging for Psychiatric Disorders: New Perspectives.